Model-assisted analysis for tuning anthocyanin composition in grape berries

Abstract

Anthocyanin composition is responsible for the red color of grape berries and wines, and contributes to their organoleptic quality. However, anthocyanin biosynthesis is under genetic, developmental and environmental regulation, making its targeted fine-tuning challenging. We constructed a mechanistic model to simulate the dynamics of anthocyanin composition throughout grape ripening in Vitis vinifera L., employing a consensus anthocyanin biosynthesis pathway. The model was calibrated and validated using 6 datasets from 8 cultivars and 37 growth conditions. Tuning the transformation and degradation parameters allowed us to accurately simulate the accumulation process of each individual anthocyanin under different environmental conditions. The model parameters were robust across environments for each genotype. The coefficients of determination (R2) for the simulated versus observed values for the 6 datasets ranged from 0.92 to 0.99, while the relative root mean square errors (RRMSEs) were between 16.8% and 42.1%. The leave-one-out cross-validation for 3 datasets showed R2 values of 0.99, 0.96, and 0.91, and RRMSE values of 28.8%, 32.9%, and 26.4%, respectively, suggesting a high prediction quality of the model. Model analysis showed that the anthocyanin profiles of diverse genotypes are relatively stable in response to parameter perturbations. Virtual experiments further suggested that targeted anthocyanin profiles may be reached by manipulating a minimum of 3 parameters, in a genotype-dependent manner. This model presents a promising methodology for characterizing the temporal progression of anthocyanin composition, while also offering a logical foundation for bioengineering endeavors focused on precisely adjusting the anthocyanin composition of grapes.